Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT

Mécanismes d'allocation de ressources et fiabilité dans les réseaux coeur de prochaines générations

Définitions et concepts de bases -- Éléments de problématique -- Objectifs de recherche -- Principales contributions -- Revue de littérature -- Modèles de services -- Routage avec Qualité de Service -- Ingénierie de traffic -- Contrôle d'admission avec Qualité de Service -- Fiabilité des réseaux -- A novel admission control mechanism in GMPLS- BASED IP over optical networks -- Problem statement -- Numerical results -- Joint routing and admission control problem under statistical delay and jitter constraints in MPLS networks -- Simulation results -- A survivable multicast routing mechanism in WDM optical networks -- Survivable routing under SRLG constraints -- GR-SMRS : Greedy heuristic for survivable multicast routing under SRLG constraints -- simulation results

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

A metaheuristic and simheuristic approach for the p-Hub median problem from a telecommunication perspective

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Avanços recentes no setor das telecomunicações oferecem grandes oportunidades para cidadãos e organizações em um mundo globalmente conectado, ao mesmo tempo em que surge um vasto número de desafios complexos que os engenheiros devem enfrentar. Alguns desses desafios podem ser modelados como problemas de otimização. Alguns exemplos incluem o problema de alocação de recursos em redes de comunicações, desenho de topologias de rede que satisfaça determinadas propriedades associadas a requisitos de qualidade de serviço, sobreposição de redes multicast e outros recursos importantes para comunicação de origem a destino. O primeiro objetivo desta tese é fornecer uma revisão sobre como as metaheurísticas têm sido usadas até agora para lidar com os problemas de otimização associados aos sistemas de telecomunicações, detectando as principais tendências e desafios. Particularmente, a análise enfoca os problemas de desenho, roteamento e alocação de recursos. Além disso, devido á natureza desses desafios, o presente trabalho discute como a hibridização de metaheurísticas com metodologias como simulação pode ser empregada para ampliar as capacidades das metaheurísticas na resolução de problemas de otimização estocásticos na indústria de telecomunicações. Logo, é analisado um problema de otimização com aplicações práticas para redes de telecomunica ções: o problema das p medianas não capacitado em que um número fixo de hubs tem capacidade ilimitada, cada nó não-hub é alocado para um único hub e o número de hubs é conhecido de antemão, sendo analisado em cenários determinísticos e estocásticos. Dada a sua variedade e importância prática, o problema das p medianas vem sendo aplicado e estudado em vários contextos. Seguidamente, propõem-se dois algoritmos imune-inspirados e uma metaheurística de dois estágios, que se baseia na combinação de técnicas tendenciosas e aleatórias com uma estrutura de busca local iterada, além de sua integração com a técnica de simulação de Monte Carlo para resolver o problema das p medianas. Para demonstrar a eficiência dos algoritmos, uma série de testes computacionais é realizada, utilizando instâncias de grande porte da literatura. Estes resultados contribuem para uma compreensão mais profunda da eficácia das metaheurísticas empregadas para resolver o problema das p medianas em redes pequenas e grandes. Por último, uma aplicaçã o ilustrativa do problema das p medianas é apresentada, bem como alguns insights sobre novas possibilidades para ele, estendendo a metodologia proposta para ambientes da vida real.Recent advances in the telecommunication industry o er great opportunities to citizens and organizations in a globally-connected world, but they also arise a vast number of complex challenges that decision makers must face. Some of these challenges can be modeled as optimization problems. Examples include the framework of network utility maximization for resource allocation in communication networks, nding a network topology that satis es certain properties associated with quality of service requirements, overlay multicast networks, and other important features for source to destination communication. First, this thesis provides a review on how metaheuristics have been used so far to deal with optimization problems associated with telecommunication systems, detecting the main trends and challenges. Particularly the analysis focuses on the network design, routing, and allocation problems. In addition, due to the nature of these challenges, this work discusses how the hybridization of metaheuristics with methodologies such as simulation can be employed to extend the capabilities of metaheuristics when solving stochastic optimization problems. Then, a popular optimization problem with practical applications to the design of telecommunication networks: the Uncapacitated Single Allocation p-Hub Median Problem (USApHMP) where a xed number of hubs have unlimited capacity, each non-hub node is allocated to a single hub and the number of hubs is known in advance is analyzed in deterministic and stochastic scenarios. p-hub median problems are concerned with optimality of telecommunication and transshipment networks, and seek to minimize the cost of transportation or establishing. Next, two immune inspired metaheuristics are proposed to solve the USApHMP, besides that, a two-stage metaheuristic which relies on the combination of biased-randomized techniques with an iterated local search framework and its integration with simulation Monte Carlo technique for solving the same problem is proposed. In order to show their e ciency, a series of computational tests are carried out using small and large size instances from the literature. These results contribute to a deeper understanding of the e ectiveness of the employed metaheuristics for solving the USApHMP in small and large networks. Finally, an illustrative application of the USApHMP is presented as well as some insights about some new possibilities for it, extending the proposed methodology to real-life environments.Els últims avenços en la industria de les telecomunicacions ofereixen grans oportunitats per ciutadans i organitzacions en un món globalment connectat, però a la vegada, presenten reptes als que s'enfronten tècnics i enginyers que prenen decisions. Alguns d'aquests reptes es poden modelitzar com problemes d'optimització. Exemples inclouen l'assignació de recursos a les xarxes de comunicació, trobant una topologia de xarxa que satisfà certes propietats associades a requisits de qualitat de servei, xarxes multicast superposades i altres funcions importants per a la comunicació origen a destinació. El primer objectiu d'aquest treball és proporcionar un revisió de la literatura sobre com s'han utilitzat aquestes tècniques, tradicionalment, per tractar els problemes d'optimització associats a sistemes de telecomunicació, detectant les principals tendències i desa aments. Particularment, l'estudi es centra en els problemes de disseny de xarxes, enrutament i problemes d'assignació de recursos. Degut a la naturalesa d'aquests problemes, aquest treball també analitza com es poden combinar les tècniques metaheurístiques amb metodologies de simulació per ampliar les capacitats de resoldre problemes d'optimització estocàstics. A més, es tracta un popular problema d'optimització amb aplicacions pràctiques per xarxes de telecomunicació, el problema de la p mediana no capacitat, analitzant-lo des d'escenaris deterministes i estocàstics. Aquest problema consisteix en determinar el nombre d'instal lacions (medianes) en una xarxa, minimitzant la suma de tots els costs o distàncies des d'un punt de demanda a la instal lació més propera. En general, el problema de la p mediana està lligat amb l'optimització de xarxes de telecomunicacions i de transport, i busquen minimitzar el cost de transport o establiment de la xarxa. Es proposa dos algoritmes immunològics i un algoritme metaheurístic de dues etapes basat en la combinació de tècniques aleatòries amb simulacions Monte Carlo. L'e ciència de les algoritmes es posa a prova mitjançant alguns dels test computacionals més utilitzats a la literatura, obtenint uns resultats molt satisfactoris, ja que es capaç de resoldre casos petits i grans en qüestió de segons i amb un baix cost computacional. Finalment, es presenta una aplicació il lustrativa del problema de la p mediana, així com algunes noves idees sobre aquest, que estenen la metodologia proposta a problemes de la vida real

A tabu search algorithm for dynamic routing in ATM cell-switching networks

This paper deals with the dynamic routing problem in ATM cell-switching networks. We present a mathematical programming model based on cell loss and a Tabu Search algorithm with short-term memory that is reinforced with a long-term memory procedure. The estimation of the quality of the solutions is fast, due to the specific encoding of the feasible solutions. The Tabu Search algorithm reaches good quality solutions, outperforming other approaches such as Genetic Algorithms and the Minimum Switching Path heuristic, regarding both cell loss and the CPU time consumption. The best results were found for the more complex networks with a high number of switches and links

Design and provisioning of WDM networks for traffic grooming

Wavelength Division Multiplexing (WDM) is the most viable technique for utilizing the enormous amounts of bandwidth inherently available in optical fibers. However, the bandwidth offered by a single wavelength in WDM networks is on the order of tens of Gigabits per second, while most of the applications\u27 bandwidth requirements are still subwavelength. Therefore, cost-effective design and provisioning of WDM networks require that traffic from different sessions share bandwidth of a single wavelength by employing electronic multiplexing at higher layers. This is known as traffic grooming. Optical networks supporting traffic grooming are usually designed in a way such that the cost of the higher layer equipment used to support a given traffic matrix is reduced. In this thesis, we propose a number of optimal and heuristic solutions for the design and provisioning of optical networks for traffic grooming with an objective of network cost reduction. In doing so, we address several practical issues. Specifically, we address the design and provisioning of WDM networks on unidirectional and bidirectional rings for arbitrary unicast traffic grooming, and on mesh topologies for arbitrary multipoint traffic grooming. In multipoint traffic grooming, we address both multicast and many-to-one traffic grooming problems. We provide a unified frame work for optimal and approximate network dimensioning and channel provisioning for the generic multicast traffic grooming problem, as well as some variants of the problem. For many-to-one traffic grooming we propose optimal as well as heuristic solutions. Optimal formulations which are inherently non-linear are mapped to an optimal linear formulation. In the heuristic solutions, we employ different problem specific search strategies to explore the solution space. We provide a number of experimental results to show the efficacy of our proposed techniques for the traffic grooming problem in WDM networks

Switching Equipment Location/Allocation in hybrid PONs

Our research goal is to investigate the FTTX (Fiber-to-the Home/Premises/Curb) passive optical network (PON) for the deployment of BISAN (Broadband Internet Subscriber Access Network) to exploit the opportunities of optical fiber enabled technologies as well as of passive switching equipment. Indeed, the deployment of FTTX PON is the most OPEX-friendly scenario, because it allows for completely passive access networks through minimizing the number of active components in the network. Previously, most FTTX PON architectures are designed based on the principle of either time division multiplexing (TDM) technology or wavelength division multiplexing (WDM) technology. We focus on designing the best possible architectures of FTTX PON, specifically hybrid PONs, which embraces both TDM and WDM technology. A hybrid PON architecture is very efficient as it is not limited to any specific PON technology, rather it is flexible enough to deploy TDM/WDM technology depending on the type (i.e unicast/multicast) and amount of traffic demand of the end-users. The advantages of a hybrid PON are of two folds: (i) it can offer increased data rate to each user by employing WDM technology, (ii) it can provide flexible bandwidth utilization by employing TDM technology. In this thesis, we concentrate on determining the optimized covering of a geographical area by a set of cost-effective hybrid PONs. We also focus on the greenfield deployment of a single hybrid PON. It should be worthy to mention that while investigating the deployment of hybrid PONs, the research community around the world considers the specifications of either the physical layer or the optical layer. But an efficient planning for PON deployment should take into account the constraints of the physical and optical layers in order that both layers can work together harmoniously. We concentrate our research on the network dimensioning and the selection as well as the placement of the switching equipment in hybrid PONs with the intention of considering the constraints of both physical and optical layers. We determine the layout of an optimized PON architecture while provisioning wavelengths in a hybrid PON. We also propose to select the switching equipment depending on the type (unicast/multicast) of traffic demand. Finally, we determine the best set of hybrid PONs along with their cascading architecture, type and location of their switching equipment while satisfying the network design constraints such as the number of output ports of the switching equipment and maximum allowed signal power loss experienced at each end user’s premises. In this thesis, we propose two novel schemes for the greenfield deployment of a single hybrid PON. The first scheme consists of two phases in which a heuristic algorithm and a novel column generation (CG) based integer linear programming (ILP) optimization model are proposed in the 1st and 2nd phase respectively. In the second scheme, a novel integrated CG based ILP cross layer optimization model is proposed for the designing of a single hybrid PON. We also propose two novel schemes to deal with the greenfield deployment of multiple hybrid PONs in a given geographical area. These two schemes determine the best set of cost-effective hybrid PONs in order to serve all the end users in a given neighborhood. The first scheme executes in four phases in which two heuristic algorithms, a CG based ILP model and an ILP optimization model are proposed in the 1st, 2nd, 3rd and 4th phase respectively. In the second scheme, an ILP model as well as a CG based ILP model, another ILP model as well as another CG based ILP model, a CG based ILP model and an ILP optimization model are proposed during four consecutive phases. Our proposed scheme can optimize the design of a set of hybrid PONs covering a given geographic area as well as the selection of the best cascading architecture 1/2/mixedstage) for each selected PON. It minimizes the overall network deployment cost based on the location of the OLT and the ONUs while granting all traffic demands. The scheme emphasizes on the optimum placement of equipment in a hybrid PON infrastructure due to the critical dependency between the network performances and a proper deployment of its equipment, which, in turn depends on the locations of the users. It is a quite powerful scheme as it can handle data instances with up to several thousands ONUs. On the basis of the computational results, the proposed scheme leads to an efficient automated tool for network design, planning, and performance evaluation which can be beneficial for the network designers